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The formatted source code for this file is here.
And a raw version here.
Previous work by Youngser Park can be found here.
Following from previous pages, this page will focus on filtering the data before clustering to explore if filtering improves the outcome of clustering.
Here we read in the data and select a random half of it for exploration.
featFull <- fread("../data/synapsinR_7thA.tif.Pivots.txt.2011Features.txt",showProgress=FALSE)
locFull <- fread("../data/synapsinR_7thA.tif.Pivots.txt",showProgress=FALSE)
### Setting a seed and creating an index vector
### to select half of the data
set.seed(2^10)
half1 <- sample(dim(featFull)[1],dim(featFull)[1]/2)
half2 <- setdiff(1:dim(featFull)[1],half1)
feat <- featFull[half1,]
loc <- locFull[half1,]
dim(feat)# [1] 559649 144
## Setting the channel names
channel <- c('Synap_1','Synap_2','VGlut1_t1','VGlut1_t2','VGlut2','Vglut3',
'psd','glur2','nmdar1','nr2b','gad','VGAT',
'PV','Gephyr','GABAR1','GABABR','CR1','5HT1A',
'NOS','TH','VACht','Synapo','tubuli','DAPI')
## Setting the channel types
channel.type <- c('ex.pre','ex.pre','ex.pre','ex.pre','ex.pre','in.pre.small',
'ex.post','ex.post','ex.post','ex.post','in.pre','in.pre',
'in.pre','in.post','in.post','in.post','in.pre.small','other',
'ex.post','other','other','ex.post','none','none')
nchannel <- length(channel)
nfeat <- ncol(feat) / nchannel
## Createing factor variables for channel and channel type sorted properly
ffchannel <- (factor(channel.type,
levels= c("ex.pre","ex.post","in.pre","in.post","in.pre.small","other","none")
))
fchannel <- as.numeric(factor(channel.type,
levels= c("ex.pre","ex.post","in.pre","in.post","in.pre.small","other","none")
))
ford <- order(fchannel)
## Setting up colors for channel types
Syncol <- c("#197300","#5ed155","#660000","#cc0000","#ff9933","mediumblue","gold")
ccol <- Syncol[fchannel]
exType <- factor(c(rep("ex",11),rep("in",6),rep("other",7)),ordered=TRUE)
exCol<-exType;levels(exCol) <- c("#197300","#990000","mediumblue");
exCol <- as.character(exCol)
fname <- as.vector(sapply(channel,function(x) paste0(x,paste0("F",0:5))))
names(feat) <- fname
fcol <- rep(ccol, each=6)
mycol <- colorpanel(100, "purple", "black", "green")
mycol2 <- matlab.like(nchannel)f <- lapply(1:6,function(x){seq(x,ncol(feat),by=nfeat)})
featF <- lapply(f,function(x){subset(feat,select=x)})
featF0 <- featF[[1]]
f01e3 <- 1e3*data.table(apply(X=featF0, 2, function(x){((x-min(x))/(max(x)-min(x)))}))
fs <- f01e3
### Taking log_10 on data with 0's removed
ans <- apply(featF0, 1, function(row){ any(row == 0)})
logF0 <- log10(featF0[!ans,])
slogF0 <- logF0[,lapply(.SD,scale, center=TRUE,scale=TRUE)]We now have the following data sets:
featF0: The feature vector looking only at the integrated brightness features.fs: The feature vector scaled between \([0,1000]\).logF0: The feature vector, with 0’s removed, then \(log_{10}\) is applied.slogF0: The feature vector, with 0’s removed, then \(log_{10}\), then scaled by subtracting the mean and dividing by the sample standard deviation.df1 <- melt(as.matrix(fs))
names(df1) <- c("ind","channel","value")
df1$type <- factor(rep(ffchannel,each=dim(fs)[1]),levels=levels(ffchannel))
lvo <- c(1:5,7:10,19,22,11:16,6,17,18,20,21,23,24)
levels(df1$channel)<-levels(df1$channel)[lvo]
ts <- 22
gg1 <- ggplot(df1, aes(x=value)) +
scale_color_manual(values=ccol[lvo]) +
scale_fill_manual(values=ccol[lvo]) +
geom_histogram(aes(y=..density..,group=channel,colour=channel),bins=100) +
geom_density(aes(group=channel, color=channel),size=1.5) +
facet_wrap( ~ channel, scale='free', ncol=6) +
theme(plot.title=element_text(size=ts),
axis.title.x=element_text(size=ts),
axis.title.y=element_text(size=ts),
legend.title=element_text(size=ts),
legend.text=element_text(size=ts-2),
axis.text=element_text(size=ts-2),
strip.text=element_text(size=ts),
legend.position='none')+
ggtitle("Kernel Density Estimates of `fs` data.")
print(gg1)fs data.cmatfs <- cor(fs)
corrplot(cmatfs,method="color",tl.col=ccol[ford], tl.cex=0.8)pcaf0 <- prcomp(featF0,scale=TRUE, center=TRUE)
pcafs <- prcomp(fs,scale=FALSE, center=FALSE)
elpcaf0 <- getElbows(pcaf0$sdev, plot=FALSE)
elpcafs <- getElbows(pcafs$sdev, plot=FALSE)We run K-means++ for \(K=2\) on the fs data.
K1 <- c(2) ## The set of K's.
#km1 <- kmeans(pcaf0$x[,1:elpcaf0[2]], centers=K1)
set.seed(2^13)
kp1 <- kmpp(fs, k=K1)corkp11 <- cor(fs[kp1$cluster == 1,])
corkp12 <- cor(fs[kp1$cluster == 2,])
par(mfrow=c(1,2))
corrplot(corkp11,method="color",tl.col=ccol[ford], tl.cex=0.8, main='Cluster 1')
corrplot(corkp12,method="color",tl.col=ccol[ford], tl.cex=0.8, main='Cluster 2')## Formatting data for heatmap
aggp <- aggregate(fs,by=list(lab=kp1$cluster),FUN=mean)
aggp <- as.matrix(aggp[,-1])
rownames(aggp) <- clusterFraction(kp1)The following are heatmaps generated from clustering via K-means++
heatmap.2(as.matrix(aggp),dendrogram='row',Colv=NA,trace="none", col=mycol,colCol=ccol[ford],cexRow=0.8, keysize=1.25,symkey=FALSE,symbreaks=FALSE,scale="none", srtCol=90,main="Heatmap of `fs` data.") # [1] "#197300" "#197300" "#197300" "#197300" "#197300"
# [6] "#5ed155" "#5ed155" "#5ed155" "#5ed155" "#5ed155"
# [11] "#5ed155" "#660000" "#660000" "#660000" "#cc0000"
# [16] "#cc0000" "#cc0000" "#ff9933" "#ff9933" "mediumblue"
# [21] "mediumblue" "mediumblue" "gold" "gold"
Percentage of data within cluster is presented on the right side of the heatmap.
Using the location data and the results of K-means++ we show a 3d scatter plot colored accoding to cluster.
set.seed(2^12)
s1 <- sample(dim(loc)[1],5e4)
locs1 <- loc[s1,]
locs1$cluster <- kp1$cluster[s1]
m <- table(kp1$cluster)/length(kp1$cluster)
plot3d(locs1$V1,locs1$V2,locs1$V3,
col=ifelse(locs1$cluster==1,'#d95f02','#6a3d9a'), #orange,purple
alpha=0.75,
xlab='x',
ylab='y',
zlab='z')
subid <- currentSubscene3d()
rglwidget(elementId="plot3dLocations")Here we look at the kernel density estimates within each cluster to compare.
df2 <- melt(as.matrix(fs))
names(df2) <- c("ind","channel","value")
df2$cluster <- kp1$cluster
df2$type <- factor(rep(ffchannel,each=dim(fs)[1]),levels=levels(ffchannel))
gg2 <- ggplot(df2, aes(x=value)) +
scale_colour_manual(values=ccol) +
scale_x_continuous(limits=c(0,400)) +
geom_histogram(aes(y=..density..,group=channel,colour=channel),bins=250) +
geom_density(aes(group=channel, color=channel),size=1.5) +
facet_grid(channel ~ cluster, scale='free') +
theme(strip.text.y=element_text(angle=0)) +
guides(col=guide_legend(ncol=1))print(gg2)fs data given cluster from km++GABABR## re-formatting data for use in lattice
d1gab <- data.table(stack(fs, select=-GABABRF0))[,.(values)]
d1gab$GABABR <- fs$GABABRF0
### Adding relationship factor variables
nd <- paste0("GABABR","~",abbreviate(channel[-which(channel=="GABABR")]))
d1gab$ind <- factor(rep(nd,each=dim(fs)[1]),ordered=TRUE,levels=nd)
names(d1gab) <- c("x","y","g")
lat1 <- xyplot(y ~ x | g, data=d1gab,
as.table=TRUE,
colramp=BTC,
pch='.',
scales = list(y = list(relation = "free"),x = list(relation = "free")),
panel=function(x,y,...){
panel.hexbinplot(x,y,..., type='g')
panel.loess(x,y,col='red', lwd=2,...)
}
)gg3 <- ggplot(data=d1gab,aes(x=x,y=y, group=g)) +
geom_point(pch='.',alpha=0.2) +
geom_hex(bins=100) +
geom_smooth(method='lm',colour='red', alpha=0.7)+
facet_wrap( ~ g, scales='free_x') print(gg3)